Citation

Zainudin, Nur Sara and Ng, Keng Hoong and Ting, Choo Yee and Khor, Kok Chin and Tong, Gee Kok and Kalid, Suraya Nurain (2023) Portfolio Construction of Good Defensive Malaysian Shariah-Compliant Stocks Using Data Mining Techniques. Journal of System and Management Sciences, 13 (5). ISSN 1816-6075, 1818-0523

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Abstract

Investors, particularly Muslims who constitute a significant portion of Malaysia’s population, seek Shariah-compliant stocks that provide stable returns and exhibit defensive behaviour during economic downturns. However, the analysis of these stocks can be challenging due to the large number of Shariah-compliant stocks listed on Bursa Malaysia. This could lead to investors refraining from investing in these stocks due to a lack of financial knowledge and time. To address this issue, this study proposes a practical approach that employs data mining techniques to aid stock portfolio construction. The approach uses the Beta coefficient to identify less volatile Shariah-compliant stocks on Bursa Malaysia from 2018-2021. The study then utilises k-Means clustering to group stocks with similar financial characteristics and selects well-performing clusters based on their financial performance. The researchers then form equal-weighted portfolios using the stocks frequently selected as members of the well-performing clusters and evaluate their performance by comparing their returns with various sectors’ price indexes. The results show that most stock portfolios outperformed the indexes. The study highlights the importance of data mining techniques in identifying good Shariah-compliant stocks and forming portfolios. Furthermore, the study provides a practical solution to investors looking to invest in Malaysian Shariah-compliant stocks

Item Type:	Article
Uncontrolled Keywords:	Stock profiling, Defensive Shariah-compliant stock, Beta coefficient, kmeans clustering
Subjects:	H Social Sciences > HD Industries. Land use. Labor > HD2321-4730.9 Industry
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	01 Nov 2023 01:56
Last Modified:	01 Nov 2023 01:56
URII:	http://shdl.mmu.edu.my/id/eprint/11819

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